Scatter plots and lines of best fit
Interpret scatter plots, correlation and lines of best fit, and use models to make predictions.
Also known as: correlation, line of best fit, two-variable data, linear models.
Key ideas
- Positive correlation: as one variable increases, the other tends to increase.
- A line of best fit's slope is the predicted change in y for each 1-unit increase in x.
- Predictions inside the data range (interpolation) are more reliable than outside it (extrapolation).
- Correlation does not prove causation.
Reading scatter plots
Each point is one observation of two variables. Describe the pattern by direction (positive or negative), strength (how close points lie to a line) and any outliers.
Lines of best fit
A model such as predicts from . Here the slope means each extra unit of is associated with an increase of about in ; the intercept is the predicted when (only meaningful if makes sense).
The residual for a point is actual predicted.
Using models carefully
Predict within the range of the data where possible. A strong correlation between ice-cream sales and sunburn doesn't mean one causes the other; both rise with sunny weather.
Worked examples
Example 1 · Interpreting a slope
A model for a plant's height is , where is height in cm and is weeks since planting. Interpret .
- Step 1.1.8 is the slope: the predicted increase in height for each additional week.
Answer: The plant is predicted to grow about 1.8 cm per week.
Example 2 · Residual
Using , a point has and . Find the residual.
- Step 1.Predicted .
- Step 2.Residual .
Answer: 3
Common mistakes
- Interpreting the intercept when x = 0 is outside the data or meaningless.
- Calling a correlation "causation".
- Computing residual as predicted − actual (the sign matters).
Try it yourself
Original practice questions. Your answer is checked instantly.
In a scatter plot, students who spent more hours revising tended to score higher on a test. What kind of correlation does this show?
A line of best fit is . What value of does it predict when ?